Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

中文翻译：

---

使用 NumPy 进行数组编程

数组编程为访问、操作和操作向量、矩阵和高维数组中的数据提供了强大、紧凑和富有表现力的语法。NumPy 是 Python 语言的主要数组编程库。它在物理学、化学、天文学、地球科学、生物学、心理学、材料科学、工程、金融和经济学等不同领域的研究分析管道中发挥着重要作用。例如，在天文学中，NumPy 是用于发现引力波 1 和首次成像黑洞 2 的软件堆栈的重要组成部分。在这里，我们回顾了一些基本的数组概念如何导致用于组织、探索和分析科学数据的简单而强大的编程范式。NumPy 是构建科学 Python 生态系统的基础。它是如此普遍，以至于有几个项目针对有特殊需求的受众，开发了自己的类似 NumPy 的接口和数组对象。由于其在生态系统中的中心地位，NumPy 越来越多地充当此类数组计算库之间的互操作层，并与其应用程序编程接口 (API) 一起提供灵活的框架来支持未来十年的科学和工业分析。